DEPICT is an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.

References:

• Tune H. Pers, Juha M. Karjalainen, Yingleong Chan, Harm-Jan Westra, Andrew R. Wood, Jian Yang, Julian C. Lui, Sailaja Vedantam, Stefan Gustafsson, Tonu Esko, Tim Frayling, Elizabeth K. Speliotes, Genetic Investigation of ANthropometric Traits (GIANT) Consortiumw, Michael Boehnke, Soumya Raychaudhuri, Rudolf S.N. Fehrmann, Joel N. Hirschhorn & Lude Franke
  Biological interpretation of genome-wide association studies using predicted gene functions
  Nature Communications 6 Article number: 5890 (2015) | DOI: 10.1038
  

• DEPICT Github page

• Module Name: depict (see the modules page for more information)
• Unusual environment variables set
  • DEPICT_HOME  installation directory
  • DEPICT_SRC       source code directory
  • DEPICT_DATA  sample data directory

Allocate an interactive session and run the program. Sample session:

[user@biowulf]$ sinteractive --mem=8g --gres=lscratch:10 -c4
[user@cn4327 ~]$module load depict  
[+] Loading singularity  4.1.5  on cn4327
[+] Loading depict v1_rel194  ...
[user@cn4327]$ mkdir -p /data/$USER/depict && cd /data/$USER/depict 
[user@cn4327]$ cp -r $DEPICT_CODE . 
[user@cn4327]$ cp $DEPICT_CONFIG .

[user@cn4327]$ depict.py ldl_teslovich_nature2010.cfg

Will store result files to /data/user/depict/example

Reading GWAS and mapping by chromosome and position to genotype data

Reading precomputed 1KG SNP collection file

Writing DEPICT loci

Retrieving background loci

Running DEPICT gene prioritization and gene set enrichment analysis

Running DEPICT tissue enrichment analysis